class_docstr: The random surrogate simply predicts random performance metrics to act as a
  baseline.
code: def _predict(self, X: List[Config[T]]) -> npt.NDArray[np.float32]:
      return np.random.rand(len(X), self.num_outputs_).astype(
          np.float32
      )
  
file_path: gluonts/src/gluonts/nursery/tsbench/src/tsbench/surrogate/random.py
imports: from typing import List
  import numpy
  import numpy.typing
  from tsbench.config import Config
  from ._base import Surrogate, T
  from ._factory import register_ensemble_surrogate, register_surrogate
label_desc: Use image node_id 2 for calling the RandomSurrogate obj's underlying member method code with example usage: obj._predict(X) and returns: np